A phase 1/2 study of durvalumab (DURVA) in combination with lenalidomide (LEN) with or without dexamethasone (DEX) in patients (pts) with newly diagnosed multiple myeloma (NDMM).
Bibliographic record
Abstract
TPS8055 Background: LEN + DEX (Rd) is approved for pts with newly diagnosed MM, including those who are transplant non-eligible (TNE). DURVA is a monoclonal antibody to programmed death ligand 1 (PD-L1) that blocks PD-L1 binding to programmed death-1 (PD-1). Preclinical studies showed anti-MM immune responses with PD-1/PD-L1 blockade that were enhanced with LEN (Görgün et al, 2015). Here, we present a phase 1/2, multicenter, open-label trial in progress (MEDI4736-MM-002) designed to evaluate DURVA in combination with LEN ± DEX in a target population of pts who are TNE and/or with high-risk NDMM. Methods: Enrollment of up to 138 pts from the US, Canada, and Europe is planned to determine the recommended dose of DURVA (primary endpoint) with LEN ± DEX for the treatment (Tx) of NDMM. Key secondary endpoints include safety, response outcomes, pharmacokinetics, progression-free survival, and overall survival. Pts with previously untreated MM with ≥ 1 of the following will be included: 1 of the CRAB criteria or clonal bone marrow plasma cells ≥ 60% and an Eastern Cooperative Oncology Group performance status of ≤ 2. Pts with a history of primary immunodeficiency will be excluded. Each independent cohort (A, B, C) will enroll 6 pts in parallel in the dose-finding phase (Table). Dose-limiting toxicities will be evaluated during the first cycle of Tx. The optimal regimen will be determined from the dose-finding phase and a parallel dose-expansion phase of up to 40 pts per cohort. Tx will continue until progressive disease or unacceptable toxicity. To date, 15 pts have enrolled. Clinical trial information: NCT02685826. [Table: see text]
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".